Classification of lung diseases from X-ray images using deep learning

The lung disease, due to COVID-19 for example, has caused devastation around the world. Even in the most developed nations, the growing number of cases has overwhelmed healthcare facilities. Radiographic imaging is still the most convenient screening method for lung diseases. A certified radiologist...

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Main Author:	Tan, Zheng Yu
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/102727/1/TanZhengYuMSKE2022.pdf.pdf
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id	my-utm-ep.102727
record_format	uketd_dc
spelling	my-utm-ep.1027272023-09-20T03:25:34Z Classification of lung diseases from X-ray images using deep learning 2022 Tan, Zheng Yu TK Electrical engineering. Electronics Nuclear engineering The lung disease, due to COVID-19 for example, has caused devastation around the world. Even in the most developed nations, the growing number of cases has overwhelmed healthcare facilities. Radiographic imaging is still the most convenient screening method for lung diseases. A certified radiologist interprets the chest X-ray image according to their experience level. As such, the interpretations might vary for different radiologists based on the observed characteristics and due to possibility of human error. To counter this problem, an automated lung disease classification system using chest X-ray was proposed. The classification was achieved by using deep learning approach because artificial intelligence has been proven to help reduce human error in medical applications. In this project, five deep learning architectures namely ResNet18, ResNet50, ResNet101, Alexnet, and VGG16 architectures were selected for transfer learning and classification of lung diseases. The lung X-ray images were classified into five output classes, namely COVID-19, pneumonia, tuberculosis, nodule or normal lungs. Images from multiple public datasets were acquired to be used as train set and test set for this automated lung disease classification model. The five deep learning models were successfully tested, and the highest accuracy was 96.3%, achieved with the Alexnet architecture. 2022 Thesis http://eprints.utm.my/102727/ http://eprints.utm.my/102727/1/TanZhengYuMSKE2022.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149729 masters Universiti Teknologi Malaysia Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Tan, Zheng Yu Classification of lung diseases from X-ray images using deep learning
description	The lung disease, due to COVID-19 for example, has caused devastation around the world. Even in the most developed nations, the growing number of cases has overwhelmed healthcare facilities. Radiographic imaging is still the most convenient screening method for lung diseases. A certified radiologist interprets the chest X-ray image according to their experience level. As such, the interpretations might vary for different radiologists based on the observed characteristics and due to possibility of human error. To counter this problem, an automated lung disease classification system using chest X-ray was proposed. The classification was achieved by using deep learning approach because artificial intelligence has been proven to help reduce human error in medical applications. In this project, five deep learning architectures namely ResNet18, ResNet50, ResNet101, Alexnet, and VGG16 architectures were selected for transfer learning and classification of lung diseases. The lung X-ray images were classified into five output classes, namely COVID-19, pneumonia, tuberculosis, nodule or normal lungs. Images from multiple public datasets were acquired to be used as train set and test set for this automated lung disease classification model. The five deep learning models were successfully tested, and the highest accuracy was 96.3%, achieved with the Alexnet architecture.
format	Thesis
qualification_level	Master's degree
author	Tan, Zheng Yu
author_facet	Tan, Zheng Yu
author_sort	Tan, Zheng Yu
title	Classification of lung diseases from X-ray images using deep learning
title_short	Classification of lung diseases from X-ray images using deep learning
title_full	Classification of lung diseases from X-ray images using deep learning
title_fullStr	Classification of lung diseases from X-ray images using deep learning
title_full_unstemmed	Classification of lung diseases from X-ray images using deep learning
title_sort	classification of lung diseases from x-ray images using deep learning
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2022
url	http://eprints.utm.my/102727/1/TanZhengYuMSKE2022.pdf.pdf
_version_	1783729213340123136

Classification of lung diseases from X-ray images using deep learning

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